20 research outputs found
How to Extract the Geometry and Topology from Very Large 3D Segmentations
Segmentation is often an essential intermediate step in image analysis. A
volume segmentation characterizes the underlying volume image in terms of
geometric information--segments, faces between segments, curves in which
several faces meet--as well as a topology on these objects. Existing algorithms
encode this information in designated data structures, but require that these
data structures fit entirely in Random Access Memory (RAM). Today, 3D images
with several billion voxels are acquired, e.g. in structural neurobiology.
Since these large volumes can no longer be processed with existing methods, we
present a new algorithm which performs geometry and topology extraction with a
runtime linear in the number of voxels and log-linear in the number of faces
and curves. The parallelizable algorithm proceeds in a block-wise fashion and
constructs a consistent representation of the entire volume image on the hard
drive, making the structure of very large volume segmentations accessible to
image analysis. The parallelized C++ source code, free command line tools and
MATLAB mex files are avilable from
http://hci.iwr.uni-heidelberg.de/software.phpComment: C++ source code, free command line tools and MATLAB mex files are
avilable from http://hci.iwr.uni-heidelberg.de/software.ph
Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x
Multi-dimensional arrays are among the most fundamental and most useful data
structures of all. In C++, excellent template libraries exist for arrays whose
dimension is fixed at runtime. Arrays whose dimension can change at runtime
have been implemented in C. However, a generic object-oriented C++
implementation of runtime-flexible arrays has so far been missing. In this
article, we discuss our new implementation called Marray, a package of class
templates that fills this gap. Marray is based on views as an underlying
concept. This concept brings some of the flexibility known from script
languages such as R and MATLAB to C++. Marray is free both for commercial and
non-commercial use and is publicly available from www.andres.sc/marrayComment: Free source code availabl
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search
This article presents a new search algorithm for the NP-hard problem of
optimizing functions of binary variables that decompose according to a
graphical model. It can be applied to models of any order and structure. The
main novelty is a technique to constrain the search space based on the topology
of the model. When pursued to the full search depth, the algorithm is
guaranteed to converge to a global optimum, passing through a series of
monotonously improving local optima that are guaranteed to be optimal within a
given and increasing Hamming distance. For a search depth of 1, it specializes
to Iterated Conditional Modes. Between these extremes, a useful tradeoff
between approximation quality and runtime is established. Experiments on models
derived from both illustrative and real problems show that approximations found
with limited search depth match or improve those obtained by state-of-the-art
methods based on message passing and linear programming.Comment: C++ Source Code available from
http://hci.iwr.uni-heidelberg.de/software.ph
Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties
Stellar feedback, the energetic interaction between young stars and their
birthplace, plays an important role in the star formation history of the
universe and the evolution of the interstellar medium (ISM). Correctly
interpreting the observations of star-forming regions is essential to
understand stellar feedback, but it is a non-trivial task due to the complexity
of the feedback processes and degeneracy in observations. In our recent paper,
we introduced a conditional invertible neural network (cINN) that predicts
seven physical properties of star-forming regions from the luminosity of 12
optical emission lines as a novel method to analyze degenerate observations. We
demonstrated that our network, trained on synthetic star-forming region models
produced by the WARPFIELD-Emission predictor (WARPFIELD-EMP), could predict
physical properties accurately and precisely. In this paper, we present a new
updated version of the cINN that takes into account the observational
uncertainties during network training. Our new network named Noise-Net reflects
the influence of the uncertainty on the parameter prediction by using both
emission-line luminosity and corresponding uncertainties as the necessary input
information of the network. We examine the performance of the Noise-Net as a
function of the uncertainty and compare it with the previous version of the
cINN, which does not learn uncertainties during the training. We confirm that
the Noise-Net outperforms the previous network for the typical observational
uncertainty range and maintains high accuracy even when subject to large
uncertainties.Comment: 22 pages, 14 figures, Accepted for publication by MNRAS on 04.
Januar
Exoplanet characterization using conditional invertible neural networks
The characterization of an exoplanet's interior is an inverse problem, which
requires statistical methods such as Bayesian inference in order to be solved.
Current methods employ Markov Chain Monte Carlo (MCMC) sampling to infer the
posterior probability of planetary structure parameters for a given exoplanet.
These methods are time consuming since they require the calculation of a large
number of planetary structure models. To speed up the inference process when
characterizing an exoplanet, we propose to use conditional invertible neural
networks (cINNs) to calculate the posterior probability of the internal
structure parameters. cINNs are a special type of neural network which excel in
solving inverse problems. We constructed a cINN using FrEIA, which was then
trained on a database of internal structure models to recover
the inverse mapping between internal structure parameters and observable
features (i.e., planetary mass, planetary radius and composition of the host
star). The cINN method was compared to a Metropolis-Hastings MCMC. For that we
repeated the characterization of the exoplanet K2-111 b, using both the MCMC
method and the trained cINN. We show that the inferred posterior probability of
the internal structure parameters from both methods are very similar, with the
biggest differences seen in the exoplanet's water content. Thus cINNs are a
possible alternative to the standard time-consuming sampling methods. Indeed,
using cINNs allows for orders of magnitude faster inference of an exoplanet's
composition than what is possible using an MCMC method, however, it still
requires the computation of a large database of internal structures to train
the cINN. Since this database is only computed once, we found that using a cINN
is more efficient than an MCMC, when more than 10 exoplanets are characterized
using the same cINN.Comment: 15 pages, 13 figures, submitted to Astronomy & Astrophysic
Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44
The Hubble Space Telescope (HST) survey Measuring Young Stars in Space and
Time (MYSST) entails some of the deepest photometric observations of
extragalactic star formation, capturing even the lowest mass stars of the
active star-forming complex N44 in the Large Magellanic Cloud. We employ the
new MYSST stellar catalog to identify and characterize the content of young
pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering
structure. To distinguish PMS stars from more evolved line of sight
contaminants, a non-trivial task due to several effects that alter photometry,
we utilize a machine learning classification approach. This consists of
training a support vector machine (SVM) and a random forest (RF) on a carefully
selected subset of the MYSST data and categorize all observed stars as PMS or
non-PMS. Combining SVM and RF predictions to retrieve the most robust set of
PMS sources, we find candidates with a PMS probability above 95%
across N44. Employing a clustering approach based on a nearest neighbor surface
density estimate, we identify 18 prominent PMS structures at
significance above the mean density with sub-clusters persisting up to and
beyond significance. The most active star-forming center, located
at the western edge of N44's bubble, is a subcluster with an effective radius
of pc entailing more than 1,100 PMS candidates. Furthermore, we
confirm that almost all identified clusters coincide with known H II regions
and are close to or harbor massive young O stars or YSOs previously discovered
by MUSE and Spitzer observations.Comment: 29 pages, 21 figures, accepted for publication in A
Measuring Young Stars in Space and Time -- I. The Photometric Catalog and Extinction Properties of N44
In order to better understand the role of high-mass stellar feedback in
regulating star formation in giant molecular clouds, we carried out a Hubble
Space Telescope (HST) Treasury Program "Measuring Young Stars in Space and
Time" (MYSST) targeting the star-forming complex N44 in the Large Magellanic
Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and
WFC3/UVIS, we built a photometric catalog of 461,684 stars down to
mag and mag,
corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of
at the LMC distance. In this first paper we describe
the observing strategy of MYSST, the data reduction procedure, and present the
photometric catalog. We identify multiple young stellar populations tracing the
gaseous rim of N44's super bubble, together with various contaminants belonging
to the LMC field population. We also determine the reddening properties from
the slope of the elongated red clump feature by applying the machine learning
algorithm RANSAC, and we select a set of Upper Main Sequence (UMS) stars as
primary probes to build an extinction map, deriving a relatively modest median
extinction mag. The same procedure applied to
the red clump provides mag.Comment: 29 pages, 15 figures, accepted for publication in A
ilastik: interactive machine learning for (bio)image analysis
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three
case studies and a discussion on the expected performance
Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images
We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection